AutoML Alex
State-of-the art Automated Machine Learning python library for Tabular Data
From AutoML-Benchmark
Scheme
Features
- Automated Data Clean (Auto Clean)
- Automated Feature Engineering (Auto FE)
- Smart Hyperparameter Optimization (HPO)
- Feature Generation
- Feature Selection
- Models Selection
- Cross Validation
- Timelimit and EarlyStoping
Installation
pip install automl-alex
🚀 Examples
Classifier:
from automl_alex import AutoMLClassifier
model = AutoMLClassifier(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)
Regression:
from automl_alex import AutoMLRegressor
model = AutoMLRegressor(X_train, y_train, X_test,)
predict_test, predict_train = model.fit_predict(timeout=2000,)
More examples in the folder ./examples:
- 01_Quick_Start.ipynb
- 02_Models.ipynb
- 03_Data_Cleaning_and_Encoding_(DataBunch).ipynb
- 04_ModelsReview.ipynb
- 05_BestSingleModel.ipynb
What's inside
It integrates many popular frameworks:
- scikit-learn
- XGBoost
- LightGBM
- CatBoost
- Optuna
- ...
Note:
- With a large dataset, a lot of memory is required! Library creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.
- Do not work with timeseries and texts yet
Road Map
-
Feature Generation
-
Advanced Logging
-
New Features Generators
-
DL Encoders
-
Save/Load and Predict on New Samples
-
Add More libs (NNs)
-
Add opt Pruners
-
Build pipelines
-
Docs Site
Contact
1 maintainer
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